Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations21721
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.8 MiB
Average record size in memory712.9 B

Variable types

Categorical5
Text4
DateTime1
Numeric9

Alerts

Category has constant value "22KT" Constant
Gwt(Grams) is highly overall correlated with Purchase Value and 4 other fieldsHigh correlation
Profit Margin % is highly overall correlated with Purchase ValueHigh correlation
Purchase Value is highly overall correlated with Gwt(Grams) and 5 other fieldsHigh correlation
Revenue Per Gram is highly overall correlated with Gwt(Grams) and 4 other fieldsHigh correlation
Sales_Value is highly overall correlated with Gwt(Grams) and 4 other fieldsHigh correlation
Weight Max is highly overall correlated with Gwt(Grams) and 4 other fieldsHigh correlation
Weight Min is highly overall correlated with Gwt(Grams) and 4 other fieldsHigh correlation
Age has 219 (1.0%) zeros Zeros
Weight Min has 1221 (5.6%) zeros Zeros

Reproduction

Analysis started2025-02-20 15:43:30.266923
Analysis finished2025-02-20 15:43:48.583556
Duration18.32 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Branch
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
MDU
9517 
CNI
7428 
CBT
4144 
SLM
 
632

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters65163
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCNI
2nd rowCNI
3rd rowCNI
4th rowCNI
5th rowMDU

Common Values

ValueCountFrequency (%)
MDU 9517
43.8%
CNI 7428
34.2%
CBT 4144
19.1%
SLM 632
 
2.9%

Length

2025-02-20T15:43:48.698699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T15:43:48.832417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mdu 9517
43.8%
cni 7428
34.2%
cbt 4144
19.1%
slm 632
 
2.9%

Most occurring characters

ValueCountFrequency (%)
C 11572
17.8%
M 10149
15.6%
D 9517
14.6%
U 9517
14.6%
N 7428
11.4%
I 7428
11.4%
B 4144
 
6.4%
T 4144
 
6.4%
S 632
 
1.0%
L 632
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65163
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 11572
17.8%
M 10149
15.6%
D 9517
14.6%
U 9517
14.6%
N 7428
11.4%
I 7428
11.4%
B 4144
 
6.4%
T 4144
 
6.4%
S 632
 
1.0%
L 632
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65163
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 11572
17.8%
M 10149
15.6%
D 9517
14.6%
U 9517
14.6%
N 7428
11.4%
I 7428
11.4%
B 4144
 
6.4%
T 4144
 
6.4%
S 632
 
1.0%
L 632
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65163
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 11572
17.8%
M 10149
15.6%
D 9517
14.6%
U 9517
14.6%
N 7428
11.4%
I 7428
11.4%
B 4144
 
6.4%
T 4144
 
6.4%
S 632
 
1.0%
L 632
 
1.0%
Distinct9358
Distinct (%)43.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2025-02-20T15:43:49.374208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters173768
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3066 ?
Unique (%)14.1%

Sample

1st rowGL-00153
2nd rowGL-00159
3rd rowGL-00216
4th rowGL-00237
5th rowGL-00417
ValueCountFrequency (%)
gl-04376 12
 
0.1%
gl-00201 12
 
0.1%
gl-01354 11
 
0.1%
gl-02093 11
 
0.1%
gl-05817 10
 
< 0.1%
gl-00410 10
 
< 0.1%
gl-00750 10
 
< 0.1%
gl-00919 10
 
< 0.1%
gl-00232 10
 
< 0.1%
gl-00270 9
 
< 0.1%
Other values (9348) 21616
99.5%
2025-02-20T15:43:50.226270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 32086
18.5%
G 21721
12.5%
L 21721
12.5%
- 21721
12.5%
1 10331
 
5.9%
2 9488
 
5.5%
3 8935
 
5.1%
7 8545
 
4.9%
5 8406
 
4.8%
6 8295
 
4.8%
Other values (3) 22519
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 173768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 32086
18.5%
G 21721
12.5%
L 21721
12.5%
- 21721
12.5%
1 10331
 
5.9%
2 9488
 
5.5%
3 8935
 
5.1%
7 8545
 
4.9%
5 8406
 
4.8%
6 8295
 
4.8%
Other values (3) 22519
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 173768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 32086
18.5%
G 21721
12.5%
L 21721
12.5%
- 21721
12.5%
1 10331
 
5.9%
2 9488
 
5.5%
3 8935
 
5.1%
7 8545
 
4.9%
5 8406
 
4.8%
6 8295
 
4.8%
Other values (3) 22519
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 173768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 32086
18.5%
G 21721
12.5%
L 21721
12.5%
- 21721
12.5%
1 10331
 
5.9%
2 9488
 
5.5%
3 8935
 
5.1%
7 8545
 
4.9%
5 8406
 
4.8%
6 8295
 
4.8%
Other values (3) 22519
13.0%
Distinct334
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size169.8 KiB
Minimum2024-01-04 00:00:00
Maximum2025-12-02 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-20T15:43:50.837049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T15:43:51.235283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Maker
Text

Distinct106
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2025-02-20T15:43:51.750602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2.6884582
Min length1

Characters and Unicode

Total characters58396
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowJ
ValueCountFrequency (%)
a 3589
 
16.5%
e 1351
 
6.2%
d_28 1068
 
4.9%
y_23 964
 
4.4%
p_16 880
 
4.1%
j_58 830
 
3.8%
d 789
 
3.6%
f 724
 
3.3%
q 558
 
2.6%
b 510
 
2.3%
Other values (96) 10458
48.1%
2025-02-20T15:43:52.254514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 12257
21.0%
A 3786
 
6.5%
2 3778
 
6.5%
1 3581
 
6.1%
3 3147
 
5.4%
7 2942
 
5.0%
8 2717
 
4.7%
6 2210
 
3.8%
4 2047
 
3.5%
5 1935
 
3.3%
Other values (25) 19996
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58396
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 12257
21.0%
A 3786
 
6.5%
2 3778
 
6.5%
1 3581
 
6.1%
3 3147
 
5.4%
7 2942
 
5.0%
8 2717
 
4.7%
6 2210
 
3.8%
4 2047
 
3.5%
5 1935
 
3.3%
Other values (25) 19996
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58396
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 12257
21.0%
A 3786
 
6.5%
2 3778
 
6.5%
1 3581
 
6.1%
3 3147
 
5.4%
7 2942
 
5.0%
8 2717
 
4.7%
6 2210
 
3.8%
4 2047
 
3.5%
5 1935
 
3.3%
Other values (25) 19996
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58396
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 12257
21.0%
A 3786
 
6.5%
2 3778
 
6.5%
1 3581
 
6.1%
3 3147
 
5.4%
7 2942
 
5.0%
8 2717
 
4.7%
6 2210
 
3.8%
4 2047
 
3.5%
5 1935
 
3.3%
Other values (25) 19996
34.2%

Category
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
22KT
21721 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters86884
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22KT
2nd row22KT
3rd row22KT
4th row22KT
5th row22KT

Common Values

ValueCountFrequency (%)
22KT 21721
100.0%

Length

2025-02-20T15:43:52.389049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T15:43:52.476278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
22kt 21721
100.0%

Most occurring characters

ValueCountFrequency (%)
2 43442
50.0%
K 21721
25.0%
T 21721
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 86884
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 43442
50.0%
K 21721
25.0%
T 21721
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 86884
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 43442
50.0%
K 21721
25.0%
T 21721
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 86884
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 43442
50.0%
K 21721
25.0%
T 21721
25.0%

Product
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
GO_EARRINGS
6202 
G CHAIN
4458 
G RING
4156 
GO_PENDANT
2227 
LD BANGLES
1947 
Other values (5)
2731 

Length

Max length11
Median length10
Mean length8.9344413
Min length6

Characters and Unicode

Total characters194065
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowG CHAIN
2nd rowG CHAIN
3rd rowG CHAIN
4th rowG CHAIN
5th rowG CHAIN

Common Values

ValueCountFrequency (%)
GO_EARRINGS 6202
28.6%
G CHAIN 4458
20.5%
G RING 4156
19.1%
GO_PENDANT 2227
 
10.3%
LD BANGLES 1947
 
9.0%
GO_BRACELET 1059
 
4.9%
GO_NECKLACE 884
 
4.1%
GO_MALAI 494
 
2.3%
GO_BESARI 284
 
1.3%
GO_WATCH 10
 
< 0.1%

Length

2025-02-20T15:43:52.591496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T15:43:52.739816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
g 8614
26.7%
go_earrings 6202
19.2%
chain 4458
13.8%
ring 4156
12.9%
go_pendant 2227
 
6.9%
ld 1947
 
6.0%
bangles 1947
 
6.0%
go_bracelet 1059
 
3.3%
go_necklace 884
 
2.7%
go_malai 494
 
1.5%
Other values (2) 294
 
0.9%

Most occurring characters

ValueCountFrequency (%)
G 32079
16.5%
N 22101
11.4%
A 18059
9.3%
R 17903
9.2%
I 15594
8.0%
E 14546
7.5%
O 11160
 
5.8%
_ 11160
 
5.8%
10561
 
5.4%
S 8433
 
4.3%
Other values (10) 32469
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 194065
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 32079
16.5%
N 22101
11.4%
A 18059
9.3%
R 17903
9.2%
I 15594
8.0%
E 14546
7.5%
O 11160
 
5.8%
_ 11160
 
5.8%
10561
 
5.4%
S 8433
 
4.3%
Other values (10) 32469
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 194065
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 32079
16.5%
N 22101
11.4%
A 18059
9.3%
R 17903
9.2%
I 15594
8.0%
E 14546
7.5%
O 11160
 
5.8%
_ 11160
 
5.8%
10561
 
5.4%
S 8433
 
4.3%
Other values (10) 32469
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 194065
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 32079
16.5%
N 22101
11.4%
A 18059
9.3%
R 17903
9.2%
I 15594
8.0%
E 14546
7.5%
O 11160
 
5.8%
_ 11160
 
5.8%
10561
 
5.4%
S 8433
 
4.3%
Other values (10) 32469
16.7%

Design
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
COIMBATORE
5512 
CASTING
4501 
BOMBAY
2508 
HANDCRAFTED
2495 
MACHINE MADE
2147 
Other values (4)
4558 

Length

Max length12
Median length10
Mean length8.7237696
Min length6

Characters and Unicode

Total characters189489
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMACHINE MADE
2nd rowMACHINE MADE
3rd rowMACHINE MADE
4th rowMACHINE MADE
5th rowMACHINE MADE

Common Values

ValueCountFrequency (%)
COIMBATORE 5512
25.4%
CASTING 4501
20.7%
BOMBAY 2508
11.5%
HANDCRAFTED 2495
11.5%
MACHINE MADE 2147
 
9.9%
GEMSTONE 1671
 
7.7%
KERALA 1463
 
6.7%
EXCLUSIVE 1067
 
4.9%
CALCUTTA 357
 
1.6%

Length

2025-02-20T15:43:52.949862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T15:43:53.114391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
coimbatore 5512
23.1%
casting 4501
18.9%
bombay 2508
10.5%
handcrafted 2495
10.5%
machine 2147
 
9.0%
made 2147
 
9.0%
gemstone 1671
 
7.0%
kerala 1463
 
6.1%
exclusive 1067
 
4.5%
calcutta 357
 
1.5%

Most occurring characters

ValueCountFrequency (%)
A 25445
13.4%
E 19240
10.2%
C 16436
8.7%
O 15203
 
8.0%
T 14893
 
7.9%
M 13985
 
7.4%
I 13227
 
7.0%
N 10814
 
5.7%
B 10528
 
5.6%
R 9470
 
5.0%
Other values (12) 40248
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 189489
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 25445
13.4%
E 19240
10.2%
C 16436
8.7%
O 15203
 
8.0%
T 14893
 
7.9%
M 13985
 
7.4%
I 13227
 
7.0%
N 10814
 
5.7%
B 10528
 
5.6%
R 9470
 
5.0%
Other values (12) 40248
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 189489
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 25445
13.4%
E 19240
10.2%
C 16436
8.7%
O 15203
 
8.0%
T 14893
 
7.9%
M 13985
 
7.4%
I 13227
 
7.0%
N 10814
 
5.7%
B 10528
 
5.6%
R 9470
 
5.0%
Other values (12) 40248
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 189489
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 25445
13.4%
E 19240
10.2%
C 16436
8.7%
O 15203
 
8.0%
T 14893
 
7.9%
M 13985
 
7.4%
I 13227
 
7.0%
N 10814
 
5.7%
B 10528
 
5.6%
R 9470
 
5.0%
Other values (12) 40248
21.2%
Distinct254
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2025-02-20T15:43:53.581310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length5
Mean length4.9000046
Min length3

Characters and Unicode

Total characters106433
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowMC CH
2nd row3 BAL KS
3rd rowMC RO
4th rowMC BO
5th rowKO C
ValueCountFrequency (%)
ca 3950
 
9.8%
cbe 3355
 
8.3%
r 2977
 
7.4%
c 1948
 
4.8%
by 1919
 
4.8%
e 1568
 
3.9%
er 1188
 
3.0%
mc 995
 
2.5%
d 760
 
1.9%
bg 734
 
1.8%
Other values (184) 20859
51.8%
2025-02-20T15:43:54.155250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18532
17.4%
C 12507
11.8%
A 10897
10.2%
B 9529
 
9.0%
E 8521
 
8.0%
R 6153
 
5.8%
S 4991
 
4.7%
L 4171
 
3.9%
T 4151
 
3.9%
M 3108
 
2.9%
Other values (19) 23873
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106433
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18532
17.4%
C 12507
11.8%
A 10897
10.2%
B 9529
 
9.0%
E 8521
 
8.0%
R 6153
 
5.8%
S 4991
 
4.7%
L 4171
 
3.9%
T 4151
 
3.9%
M 3108
 
2.9%
Other values (19) 23873
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106433
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18532
17.4%
C 12507
11.8%
A 10897
10.2%
B 9529
 
9.0%
E 8521
 
8.0%
R 6153
 
5.8%
S 4991
 
4.7%
L 4171
 
3.9%
T 4151
 
3.9%
M 3108
 
2.9%
Other values (19) 23873
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106433
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18532
17.4%
C 12507
11.8%
A 10897
10.2%
B 9529
 
9.0%
E 8521
 
8.0%
R 6153
 
5.8%
S 4991
 
4.7%
L 4171
 
3.9%
T 4151
 
3.9%
M 3108
 
2.9%
Other values (19) 23873
22.4%

Pcs
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0290502
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.8 KiB
2025-02-20T15:43:54.263950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.19720297
Coefficient of variation (CV)0.1916359
Kurtosis365.25495
Mean1.0290502
Median Absolute Deviation (MAD)0
Skewness12.889686
Sum22352
Variance0.03888901
MonotonicityNot monotonic
2025-02-20T15:43:54.380057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 21149
97.4%
2 533
 
2.5%
3 31
 
0.1%
4 5
 
< 0.1%
10 1
 
< 0.1%
5 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
1 21149
97.4%
2 533
 
2.5%
3 31
 
0.1%
4 5
 
< 0.1%
5 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 1
 
< 0.1%
5 1
 
< 0.1%
4 5
 
< 0.1%
3 31
 
0.1%
2 533
 
2.5%
1 21149
97.4%

Gwt(Grams)
Real number (ℝ)

High correlation 

Distinct3104
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2344856
Minimum0.1
Maximum51.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.8 KiB
2025-02-20T15:43:54.544746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.95
Q12.55
median4.71
Q312.17
95-th percentile32.05
Maximum51.92
Range51.82
Interquartile range (IQR)9.62

Descriptive statistics

Standard deviation9.8051314
Coefficient of variation (CV)1.0617951
Kurtosis2.4754746
Mean9.2344856
Median Absolute Deviation (MAD)3.31
Skewness1.7097499
Sum200582.26
Variance96.140601
MonotonicityNot monotonic
2025-02-20T15:43:54.747022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 153
 
0.7%
4.05 134
 
0.6%
4.06 129
 
0.6%
1.02 125
 
0.6%
2 122
 
0.6%
4.08 120
 
0.6%
2.05 120
 
0.6%
4.04 113
 
0.5%
4.1 110
 
0.5%
4.03 108
 
0.5%
Other values (3094) 20487
94.3%
ValueCountFrequency (%)
0.1 1
 
< 0.1%
0.11 1
 
< 0.1%
0.12 9
 
< 0.1%
0.13 10
 
< 0.1%
0.14 26
0.1%
0.15 26
0.1%
0.16 52
0.2%
0.17 13
 
0.1%
0.18 6
 
< 0.1%
0.19 4
 
< 0.1%
ValueCountFrequency (%)
51.92 1
< 0.1%
51.73 1
< 0.1%
51.67 1
< 0.1%
50.87 2
< 0.1%
50.36 1
< 0.1%
50.13 1
< 0.1%
50 1
< 0.1%
49.35 1
< 0.1%
49.26 1
< 0.1%
49.07 1
< 0.1%

Sales_Value
Real number (ℝ)

High correlation 

Distinct20943
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54194.255
Minimum3749.99
Maximum286590.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.8 KiB
2025-02-20T15:43:54.946902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3749.99
5-th percentile8992.36
Q118235.7
median30528.09
Q371279.46
95-th percentile174750.34
Maximum286590.5
Range282840.51
Interquartile range (IQR)53043.76

Descriptive statistics

Standard deviation52873.527
Coefficient of variation (CV)0.97562974
Kurtosis2.5382574
Mean54194.255
Median Absolute Deviation (MAD)17320.58
Skewness1.7134048
Sum1.1771534 × 109
Variance2.7956098 × 109
MonotonicityNot monotonic
2025-02-20T15:43:55.160751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25749.9 7
 
< 0.1%
25749.67 5
 
< 0.1%
46750.28 5
 
< 0.1%
14750.46 4
 
< 0.1%
26750.45 4
 
< 0.1%
14950.36 4
 
< 0.1%
46750.08 4
 
< 0.1%
15250.43 4
 
< 0.1%
25749.7 4
 
< 0.1%
25749.62 4
 
< 0.1%
Other values (20933) 21676
99.8%
ValueCountFrequency (%)
3749.99 1
< 0.1%
3750 1
< 0.1%
4449.63 1
< 0.1%
4455 1
< 0.1%
4500.09 1
< 0.1%
4500.2 1
< 0.1%
4500.38 1
< 0.1%
4501.79 1
< 0.1%
4502.61 1
< 0.1%
4509.14 1
< 0.1%
ValueCountFrequency (%)
286590.5 1
< 0.1%
284402.11 1
< 0.1%
284090.15 1
< 0.1%
281250.32 1
< 0.1%
281249.8 1
< 0.1%
280949.89 1
< 0.1%
280749.95 1
< 0.1%
279833.55 1
< 0.1%
279329.66 1
< 0.1%
278596.46 1
< 0.1%

Age
Real number (ℝ)

Zeros 

Distinct1560
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean193.38437
Minimum-133
Maximum19390
Zeros219
Zeros (%)1.0%
Negative1
Negative (%)< 0.1%
Memory size169.8 KiB
2025-02-20T15:43:55.367734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-133
5-th percentile4
Q129
median73
Q3172
95-th percentile817
Maximum19390
Range19523
Interquartile range (IQR)143

Descriptive statistics

Standard deviation461.85472
Coefficient of variation (CV)2.3882732
Kurtosis548.06307
Mean193.38437
Median Absolute Deviation (MAD)55
Skewness15.696018
Sum4200502
Variance213309.78
MonotonicityNot monotonic
2025-02-20T15:43:55.579141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 282
 
1.3%
2 258
 
1.2%
3 239
 
1.1%
4 237
 
1.1%
5 232
 
1.1%
0 219
 
1.0%
6 215
 
1.0%
7 203
 
0.9%
10 186
 
0.9%
16 181
 
0.8%
Other values (1550) 19469
89.6%
ValueCountFrequency (%)
-133 1
 
< 0.1%
0 219
1.0%
1 282
1.3%
2 258
1.2%
3 239
1.1%
4 237
1.1%
5 232
1.1%
6 215
1.0%
7 203
0.9%
8 176
0.8%
ValueCountFrequency (%)
19390 1
< 0.1%
19330 1
< 0.1%
19199 1
< 0.1%
19147 1
< 0.1%
3870 2
< 0.1%
3869 1
< 0.1%
3865 1
< 0.1%
3849 1
< 0.1%
3831 1
< 0.1%
3827 1
< 0.1%
Distinct97
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-02-20T15:43:55.922239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length15
Mean length9.6061876
Min length4

Characters and Unicode

Total characters208656
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowRAJAPRIYA
2nd rowMUTHUMATHI
3rd rowARUNACHALAM
4th rowMANIKANDAN
5th rowSELVAM
ValueCountFrequency (%)
kumar 2146
 
8.0%
ravikumar 1552
 
5.8%
manoj 1018
 
3.8%
arun 980
 
3.7%
rajaprabhu 893
 
3.3%
ramamoorthy 856
 
3.2%
raguram 763
 
2.8%
kamatchi 757
 
2.8%
elackiya 754
 
2.8%
muthu 697
 
2.6%
Other values (103) 16427
61.2%
2025-02-20T15:43:56.394832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 47609
22.8%
R 18345
 
8.8%
M 17683
 
8.5%
N 14520
 
7.0%
U 13068
 
6.3%
H 12440
 
6.0%
I 12063
 
5.8%
K 11391
 
5.5%
T 7622
 
3.7%
S 6111
 
2.9%
Other values (17) 47804
22.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 208656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 47609
22.8%
R 18345
 
8.8%
M 17683
 
8.5%
N 14520
 
7.0%
U 13068
 
6.3%
H 12440
 
6.0%
I 12063
 
5.8%
K 11391
 
5.5%
T 7622
 
3.7%
S 6111
 
2.9%
Other values (17) 47804
22.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 208656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 47609
22.8%
R 18345
 
8.8%
M 17683
 
8.5%
N 14520
 
7.0%
U 13068
 
6.3%
H 12440
 
6.0%
I 12063
 
5.8%
K 11391
 
5.5%
T 7622
 
3.7%
S 6111
 
2.9%
Other values (17) 47804
22.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 208656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 47609
22.8%
R 18345
 
8.8%
M 17683
 
8.5%
N 14520
 
7.0%
U 13068
 
6.3%
H 12440
 
6.0%
I 12063
 
5.8%
K 11391
 
5.5%
T 7622
 
3.7%
S 6111
 
2.9%
Other values (17) 47804
22.9%

Purchase Value
Real number (ℝ)

High correlation 

Distinct13099
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39016.188
Minimum3750
Maximum238968.79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.8 KiB
2025-02-20T15:43:56.558727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3750
5-th percentile3750
Q14459.92
median20432
Q349722
95-th percentile149419
Maximum238968.79
Range235218.79
Interquartile range (IQR)45262.08

Descriptive statistics

Standard deviation47282.584
Coefficient of variation (CV)1.211871
Kurtosis3.2185868
Mean39016.188
Median Absolute Deviation (MAD)16682
Skewness1.8847459
Sum8.4747061 × 108
Variance2.2356428 × 109
MonotonicityNot monotonic
2025-02-20T15:43:56.774040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3750 5383
 
24.8%
4586.5 40
 
0.2%
6419.09 18
 
0.1%
24261.45 16
 
0.1%
4491 16
 
0.1%
8868.69 14
 
0.1%
5723 14
 
0.1%
6702.88 12
 
0.1%
5675 11
 
0.1%
14021.4 11
 
0.1%
Other values (13089) 16186
74.5%
ValueCountFrequency (%)
3750 5383
24.8%
3762.24 1
 
< 0.1%
4166.36 1
 
< 0.1%
4193.39 1
 
< 0.1%
4203.03 1
 
< 0.1%
4206.8 2
 
< 0.1%
4208.89 1
 
< 0.1%
4212.66 2
 
< 0.1%
4218.53 1
 
< 0.1%
4317.63 1
 
< 0.1%
ValueCountFrequency (%)
238968.79 1
< 0.1%
238685.4 1
< 0.1%
238235.68 1
< 0.1%
238163.34 1
< 0.1%
238109.11 1
< 0.1%
237981.84 1
< 0.1%
237475 1
< 0.1%
237235.2 1
< 0.1%
236236 1
< 0.1%
235802.51 1
< 0.1%

Weight Min
Real number (ℝ)

High correlation  Zeros 

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6679711
Minimum0
Maximum52
Zeros1221
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size169.8 KiB
2025-02-20T15:43:56.952446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q310
95-th percentile28
Maximum52
Range52
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.4512384
Coefficient of variation (CV)1.1021479
Kurtosis3.4666872
Mean7.6679711
Median Absolute Deviation (MAD)3
Skewness1.9083887
Sum166556
Variance71.42343
MonotonicityNot monotonic
2025-02-20T15:43:57.118701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
5 3931
18.1%
3 2724
12.5%
1 2138
9.8%
2 1923
8.9%
6 1393
 
6.4%
0 1221
 
5.6%
20 1052
 
4.8%
10 1011
 
4.7%
4 960
 
4.4%
14 807
 
3.7%
Other values (19) 4561
21.0%
ValueCountFrequency (%)
0 1221
 
5.6%
0.5 474
 
2.2%
1 2138
9.8%
2 1923
8.9%
3 2724
12.5%
4 960
 
4.4%
5 3931
18.1%
6 1393
 
6.4%
7 390
 
1.8%
8 680
 
3.1%
ValueCountFrequency (%)
52 1
 
< 0.1%
48 9
 
< 0.1%
44 35
 
0.2%
42 1
 
< 0.1%
40 149
0.7%
36 352
1.6%
34 2
 
< 0.1%
32 310
1.4%
28 316
1.5%
26 1
 
< 0.1%

Weight Max
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.559988
Minimum0.5
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.8 KiB
2025-02-20T15:43:57.289806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1
Q13
median6
Q312
95-th percentile32
Maximum56
Range55.5
Interquartile range (IQR)9

Descriptive statistics

Standard deviation9.8351533
Coefficient of variation (CV)1.028783
Kurtosis3.1251186
Mean9.559988
Median Absolute Deviation (MAD)3
Skewness1.8730735
Sum207652.5
Variance96.730241
MonotonicityNot monotonic
2025-02-20T15:43:57.454259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
6 4499
20.7%
4 2949
13.6%
2 2114
9.7%
3 1698
 
7.8%
8 1676
 
7.7%
1 1431
 
6.6%
16 1310
 
6.0%
24 1017
 
4.7%
12 1011
 
4.7%
32 681
 
3.1%
Other values (22) 3335
15.4%
ValueCountFrequency (%)
0.5 228
 
1.0%
1 1431
 
6.6%
1.5 25
 
0.1%
2 2114
9.7%
3 1698
 
7.8%
4 2949
13.6%
5 392
 
1.8%
6 4499
20.7%
7 134
 
0.6%
8 1676
 
7.7%
ValueCountFrequency (%)
56 6
 
< 0.1%
52 4
 
< 0.1%
50 1
 
< 0.1%
48 157
 
0.7%
44 27
 
0.1%
42 2
 
< 0.1%
40 628
2.9%
36 34
 
0.2%
34 1
 
< 0.1%
32 681
3.1%

Profit Margin %
Real number (ℝ)

High correlation 

Distinct21638
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.776264
Minimum-1157.9897
Maximum98.691513
Zeros1
Zeros (%)< 0.1%
Negative1260
Negative (%)5.8%
Memory size169.8 KiB
2025-02-20T15:43:57.640851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1157.9897
5-th percentile-0.60906152
Q15.2410775
median9.8295583
Q343.604867
95-th percentile95.058039
Maximum98.691513
Range1256.6812
Interquartile range (IQR)38.36379

Descriptive statistics

Standard deviation37.224538
Coefficient of variation (CV)1.3902066
Kurtosis63.645348
Mean26.776264
Median Absolute Deviation (MAD)5.8411007
Skewness-1.6744666
Sum581607.23
Variance1385.6663
MonotonicityNot monotonic
2025-02-20T15:43:57.856914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94.40993594 4
 
< 0.1%
85.32265501 4
 
< 0.1%
85.43683665 3
 
< 0.1%
85.43700066 3
 
< 0.1%
85.43667829 3
 
< 0.1%
5.996752663 3
 
< 0.1%
6.454231499 2
 
< 0.1%
74.57558171 2
 
< 0.1%
85.14826193 2
 
< 0.1%
85.08956401 2
 
< 0.1%
Other values (21628) 21693
99.9%
ValueCountFrequency (%)
-1157.989723 1
< 0.1%
-745.832046 1
< 0.1%
-554.8766277 1
< 0.1%
-532.8610756 1
< 0.1%
-507.0125307 1
< 0.1%
-383.651017 1
< 0.1%
-300.2868052 1
< 0.1%
-283.466067 1
< 0.1%
-249.9737615 1
< 0.1%
-245.1506633 1
< 0.1%
ValueCountFrequency (%)
98.6915128 1
< 0.1%
98.67999647 1
< 0.1%
98.66666818 1
< 0.1%
98.6652424 1
< 0.1%
98.65991765 1
< 0.1%
98.65750025 1
< 0.1%
98.65112737 1
< 0.1%
98.64750632 1
< 0.1%
98.64514546 1
< 0.1%
98.63097231 1
< 0.1%

Revenue Per Gram
Real number (ℝ)

High correlation 

Distinct21652
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6972.6807
Minimum221.23835
Maximum55113.154
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.8 KiB
2025-02-20T15:43:58.062211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum221.23835
5-th percentile5293.1484
Q15757.0131
median6352.2983
Q37183.6082
95-th percentile9973.7347
Maximum55113.154
Range54891.916
Interquartile range (IQR)1426.5952

Descriptive statistics

Standard deviation2804.9379
Coefficient of variation (CV)0.40227539
Kurtosis52.88802
Mean6972.6807
Median Absolute Deviation (MAD)671.0789
Skewness6.2635195
Sum1.514536 × 108
Variance7867676.4
MonotonicityNot monotonic
2025-02-20T15:43:58.273192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5646.743266 4
 
< 0.1%
6373.641089 3
 
< 0.1%
5446.866145 3
 
< 0.1%
5634.470076 2
 
< 0.1%
6589.864078 2
 
< 0.1%
9132.981818 2
 
< 0.1%
7374.745 2
 
< 0.1%
9559.127451 2
 
< 0.1%
7258.009217 2
 
< 0.1%
7449.40404 2
 
< 0.1%
Other values (21642) 21697
99.9%
ValueCountFrequency (%)
221.2383481 1
< 0.1%
789.4736842 1
< 0.1%
1388.770916 1
< 0.1%
2042.361416 1
< 0.1%
2978.620227 1
< 0.1%
3504.152581 1
< 0.1%
3566.657089 1
< 0.1%
3621.660759 1
< 0.1%
3708.70697 1
< 0.1%
3710.273115 1
< 0.1%
ValueCountFrequency (%)
55113.15385 1
< 0.1%
47159.6 1
< 0.1%
45824.25 1
< 0.1%
40451.18182 1
< 0.1%
40049.66667 1
< 0.1%
38335.33333 1
< 0.1%
37917.58333 1
< 0.1%
37576.16667 1
< 0.1%
37521.75 1
< 0.1%
37514.91667 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Slow Moving
16084 
Fast Moving
5637 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters238931
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSlow Moving
2nd rowFast Moving
3rd rowFast Moving
4th rowFast Moving
5th rowFast Moving

Common Values

ValueCountFrequency (%)
Slow Moving 16084
74.0%
Fast Moving 5637
 
26.0%

Length

2025-02-20T15:43:58.460008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T15:43:58.561771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
moving 21721
50.0%
slow 16084
37.0%
fast 5637
 
13.0%

Most occurring characters

ValueCountFrequency (%)
o 37805
15.8%
21721
9.1%
M 21721
9.1%
v 21721
9.1%
i 21721
9.1%
n 21721
9.1%
g 21721
9.1%
S 16084
6.7%
l 16084
6.7%
w 16084
6.7%
Other values (4) 22548
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 238931
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 37805
15.8%
21721
9.1%
M 21721
9.1%
v 21721
9.1%
i 21721
9.1%
n 21721
9.1%
g 21721
9.1%
S 16084
6.7%
l 16084
6.7%
w 16084
6.7%
Other values (4) 22548
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 238931
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 37805
15.8%
21721
9.1%
M 21721
9.1%
v 21721
9.1%
i 21721
9.1%
n 21721
9.1%
g 21721
9.1%
S 16084
6.7%
l 16084
6.7%
w 16084
6.7%
Other values (4) 22548
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 238931
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 37805
15.8%
21721
9.1%
M 21721
9.1%
v 21721
9.1%
i 21721
9.1%
n 21721
9.1%
g 21721
9.1%
S 16084
6.7%
l 16084
6.7%
w 16084
6.7%
Other values (4) 22548
9.4%

Interactions

2025-02-20T15:43:46.418109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-20T15:43:46.232384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-20T15:43:58.682421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeBranchDesignGwt(Grams)PcsProductProduct PerformanceProfit Margin %Purchase ValueRevenue Per GramSales_ValueWeight MaxWeight Min
Age1.0000.0500.066-0.0650.0680.0470.0680.294-0.1960.069-0.062-0.021-0.016
Branch0.0501.0000.1080.0660.0020.0860.1500.0000.0560.0630.0690.0520.049
Design0.0660.1081.0000.1820.0730.3620.0980.0130.1720.1850.1720.1800.187
Gwt(Grams)-0.0650.0660.1821.0000.0300.3240.0730.0760.583-0.9010.9960.8880.878
Pcs0.0680.0020.0730.0301.0000.1290.0310.0030.030-0.0040.0320.0400.039
Product0.0470.0860.3620.3240.1291.0000.1180.0210.2820.3520.3080.3250.334
Product Performance0.0680.1500.0980.0730.0310.1181.0000.0000.1100.0580.0700.0620.057
Profit Margin %0.2940.0000.0130.0760.0030.0210.0001.000-0.5650.0690.1030.0460.046
Purchase Value-0.1960.0560.1720.5830.0300.2820.110-0.5651.000-0.5110.5840.5250.520
Revenue Per Gram0.0690.0630.185-0.901-0.0040.3520.0580.069-0.5111.000-0.869-0.796-0.786
Sales_Value-0.0620.0690.1720.9960.0320.3080.0700.1030.584-0.8691.0000.8860.876
Weight Max-0.0210.0520.1800.8880.0400.3250.0620.0460.525-0.7960.8861.0000.993
Weight Min-0.0160.0490.1870.8780.0390.3340.0570.0460.520-0.7860.8760.9931.000

Missing values

2025-02-20T15:43:48.036685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-20T15:43:48.356626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

BranchBill NoBill_dateMakerCategoryProductDesignSub_DesignPcsGwt(Grams)Sales_ValueAgeEmployeePurchase ValueWeight MinWeight MaxProfit Margin %Revenue Per GramProduct Performance
0CNIGL-0015308/04/2024A22KTG CHAINMACHINE MADEMC CH14.0525049.86216RAJAPRIYA22553.002.04.09.9675616185.150617Slow Moving
1CNIGL-0015908/04/2024A22KTG CHAINMACHINE MADE3 BAL KS140.58218750.047MUTHUMATHI205343.4336.040.06.1287355390.587482Fast Moving
2CNIGL-0021610/04/2024A22KTG CHAINMACHINE MADEMC RO112.0368750.2010ARUNACHALAM3750.0010.012.094.5454705714.896093Fast Moving
3CNIGL-0023711/04/2024A22KTG CHAINMACHINE MADEMC BO123.94129720.6314MANIKANDAN124371.4420.024.04.1236235418.572682Fast Moving
4MDUGL-0041714/04/2024J22KTG CHAINMACHINE MADEKO C139.44229750.430SELVAM207175.0436.040.09.8260495825.315162Fast Moving
5MDUGL-0042214/04/2024A22KTG CHAINMACHINE MADEMC KA18.5254454.0617RAVIKUMAR46222.646.08.015.1162656391.321596Fast Moving
6CNIGL-0034918/04/2024A22KTG CHAINMACHINE MADE3 BAL K124.15133750.4217PRAKASH123722.0620.024.07.4978165538.319669Fast Moving
7CNIGL-0046025/04/2024A22KTG CHAINMACHINE MADEMC RO18.0047112.5525RAVIKUMAR3750.006.08.092.0403375889.068750Fast Moving
8CNIGL-0048127/04/2024D22KTG CHAINMACHINE MADEMC CH19.6355460.77179VENKATESH49558.008.010.010.6431455759.166147Slow Moving
9CNIGL-0049627/04/2024A22KTG CHAINMACHINE MADEMC DE116.1789638.08369RAJAPRIYA79053.0014.016.011.8086875543.480519Slow Moving
BranchBill NoBill_dateMakerCategoryProductDesignSub_DesignPcsGwt(Grams)Sales_ValueAgeEmployeePurchase ValueWeight MinWeight MaxProfit Margin %Revenue Per GramProduct Performance
21711SLMGL-0043201/02/2025P_1622KTGO_MALAICOIMBATOREVA MA121.49135240.3956VANI PRABHA117098.5316.020.013.4145286293.177757Slow Moving
21712SLMGL-0044604/02/2025P_1622KTGO_MALAICOIMBATOREVA MA121.38132814.6359VANI PRABHA116518.6116.020.012.2697486212.096819Slow Moving
21713MDUGL-1089825/02/2025P_1622KTGO_MALAICOIMBATOREVA MA137.40223994.5180RAGURAM201014.9932.040.010.2589665989.158021Slow Moving
21714CNIGL-0813310/02/2025J_5822KTGO_MALAICOIMBATOREKA MAA130.12183751.38202RAMANATHAN155610.2624.032.015.3147806100.643426Slow Moving
21715CNIGL-0798002/02/2025Y_2322KTGO_MALAICOIMBATORELA MAA132.92202399.8295PRABU KUMAR166801.2024.032.017.5882676148.232685Slow Moving
21716CNIGL-0807706/02/2025M_8522KTGO_MALAICOIMBATORELA MAA130.52200558.4458KAMATCHI164671.4624.032.017.8935286571.377457Slow Moving
21717CBTGL-0398406/02/2025T_9122KTGO_MALAICOIMBATORELA MAA142.60255595.31146RAVIKUMAR217655.9332.040.014.8435355999.889906Slow Moving
21718CNIGL-0808206/02/2025M_8522KTGO_MALAICOIMBATORELA MAA138.08229450.4358PRABU KUMAR204527.7832.040.010.8618896025.483981Slow Moving
21719CNIGL-0811308/02/2025P_1622KTGO_MALAICOIMBATORELA MAA122.28137750.50112KAMATCHI113879.7520.024.017.3289756182.697487Slow Moving
21720MDUGL-1064515/02/2025T_9122KTGO_MALAICOIMBATORELA MAA135.18224948.35155KAVITHA180398.6632.040.019.8044086394.211200Slow Moving